机器学习在中国区域极端气候指数集合预估中的应用

doi:10.11755/j.issn.1006-7639-2026-03-0398

摘要/Abstract

摘要：

在气候变暖背景下，极端气候事件的变化趋势备受关注。基于3种机器学习（随机森林、极端随机树和岭回归）模型评估其对中国区域极端气候指数[暖昼指数（TX90p）、冷夜指数（TN10p）、日最大降水量（RX1day）及5 d最大降水量（RX5day）]的模拟能力，并与传统全局偏差订正后的多模式集合方法对比，确定最优模型方案；进一步分析所选取的极端气候指数在不同排放情景（SSP1-2.6、SSP2-4.5、SSP5-8.5）下2024—2100年相对于基准期（1961—1990年）的空间分布及其变化趋势。结果表明：基于机器学习的极端气候指数模拟方案能够在不同程度上提升对极端气候事件的模拟能力，有效减小模拟偏差；2024—2100年中国地区极端暖事件在各排放情景下均显著上升，且高排放（SSP5-8.5）下增幅最大（相较基准期上升约52%），极端冷事件显著减少，且随排放增加减少更明显；空间分布上，TX90p在华北、长江中下游、四川盆地及华南部分地区增幅相对明显，而青藏高原及西北部分高海拔地区增幅相对较小；极端降水指数（RX1day、RX5day）在所有排放情景下均呈增加趋势，且高排放情景下增加最明显，其中RX5day的增强幅度整体高于RX1day；东北和华北地区在极端降水（特别RX5day）上的响应最强，对气候变暖的敏感性更高。

关键词: 机器学习, 极端气候指数, 未来预估

Abstract:

Under the background of climate warming, changes in extreme climate events have received increasing attention. In this study, three machine learning models, namely Random Forest, Extremely Randomized Trees, and Ridge Regression, were used to evaluate their capability in simulating extreme climate indices over China, including the warm day index (TX90p), cold night index (TN10p), maximum 1-day precipitation (RX1day), and maximum consecutive 5-day precipitation (RX5day). Their performances were compared with that of the traditional globally bias-corrected multi-model ensemble method to determine the optimal model schemes. Furthermore, the spatial distributions and temporal trends of the selected extreme climate indices during 2024-2100 under different emission scenarios, including SSP1-2.6, SSP2-4.5, and SSP5-8.5, were analyzed relative to the baseline period of 1961-1990. The results show that the machine-learning-based schemes can improve the simulation capability for extreme climate events to varying degrees and effectively reduce simulation biases. During 2024-2100, extremely warm events over China increase significantly under all emission scenarios, with the largest increase under the high-emission scenario SSP5-8.5, reaching approximately 52% relative to the baseline period. In contrast, extremely cold events decrease significantly, and the reduction becomes more pronounced with increasing emissions. Spatially, TX90p shows relatively large increases in North China, the middle and lower reaches of the Yangtze River, the Sichuan Basin, and parts of South China, while the increases are relatively smaller over the Qinghai-Xizang Plateau and some high-elevation areas in Northwest China. Extreme precipitation indices, including RX1day and RX5day, show increasing trends under all emission scenarios, with the most pronounced increase under the high-emission scenario. The enhancement of RX5day is generally stronger than that of RX1day. Northeast China and North China show the strongest responses in extreme precipitation, especially for RX5day, indicating higher sensitivity to climate warming.

Key words: machine learning, extreme climate indices, future projection

中图分类号:

P467

刘明铭, 徐影. 机器学习在中国区域极端气候指数集合预估中的应用[J]. 干旱气象, 2026, 44(3): 398-411.

LIU Mingming, XU Ying. Application of machine learning in the ensemble projection of regional extreme climate indices over China[J]. Journal of Arid Meteorology, 2026, 44(3): 398-411.

图/表 9

表1 4个极端气候指数的定义（ETCCDI）

Tab.1 Definitions of the four extreme climate indices recommended by the ETCCDI

指数缩写	指数名称	定义	单位
TX90p	暖昼指数	每月日最高气温大于基准期内90%分位值的天数百分率	%
TN10p	冷夜指数	每月日最低气温小于基准期内10%分位值的天数百分率	%
RX5day	5 d最大降水量	每月最大的连续5 d降水量	mm
RX1day	日最大降水量	每月最大日降水量	mm

表2 19个CMIP6模式的基本信息

Tab.2 Basic information of the 19 CMIP6 global climate models （GCMs）

序号	模式名称	模式单位（国家）	分辨率（经度×纬度）
1	ACCESS-CM2	澳大利亚气候科学研究中心（澳大利亚）	1.25°×1.875°
2	ACCESS-ESM1-5	澳大利亚气候科学研究中心（澳大利亚）	1.25°×1.875°
3	BCC-CSM2-MR	北京气候中心（中国）	1.125°×1.125°
4	CanESM5	加拿大气候模型与分析中心（加拿大）	2.8°×2.8°
5	CNRM-CM6-1	法国国家气象研究中心（法国）	1.4°×1.4°
6	CNRM-ESM2-1	法国国家气象研究中心（法国）	1.4°×1.4°
7	EC-Earth3	EC-Earth联盟（欧洲）	0.7°×0.7°
8	FGOALS-g3	中国科学院大气物理研究所（中国）	2.25°×2.25°
9	GFDL-ESM4	地球物理流体动力学实验室（美国）	1.0°×1.25°
10	HadGEM3-GC31-LL	英国气象局（英国）	1.0°×1.0°
11	INM-CM4-8	俄罗斯数值数学研究所（俄罗斯）	1.5°×1.5°
12	KIOST-ESM	韩国海洋科学技术院（韩国）	2.0°×2.0°
13	MIROC6	日本海洋地球科学技术研究所（日本）	1.0°×1.0°
14	MPI-ESM1-2-HR	马克斯·普朗克气象研究所（德国）	1.5°×1.5°
15	MPI-ESM1-2-LR	马克斯·普朗克气象研究所（德国）	1.5°×1.5°
16	MRI-ESM2-0	气象研究所（日本）	1.125°×1.125°
17	NESM3	国家气候中心（中国）	0.75°×0.75°
18	NorESM2-LM	挪威气候中心（挪威）	1.25°×1.25°
19	UKESM1-0-LL	英国气象局（英国）	0.5°×0.5°

图1 研究框架

Fig.1 Research framework

图2 中国区域验证期（2005—2014年）不同方案在各极端气候指数模拟中的气候态偏差（模拟值减观测值）空间分布及对应的TSS评分（红色数字表示此方案TSS评分最高）

Fig.2 Spatial distribution of climate state bias （simulation values minus observed values） of different schemes in various extreme climate index simulations and the corresponding TSS scores during the verification period （2005-2014） in China （The highest TSS score in each index is highlighted in red）

图3 验证期4个极端气候指数的泰勒图比较（a）TX90p，（b）TN10p，（c）RX1day，（d）RX5day [每个子图展示了单个CMIP6模式（灰色标记）及不同方案（彩色标记）与观测的空间相关系数、标准差及中心化均方根误差（红色虚线）的分布]

Fig.3 Taylor diagrams comparison of four extreme climate indices during the verification period （a） TX90p，（b） TN10p，（c） RX1day，（d） RX5day （Each subplot shows the distribution of spatial correlation coefficient， standard deviation， and centered root mean square error （CRMSE）（red dashed lines） for a single CMIP6 model （gray markers） and different scenarios （colored markers） compared with observations）

图4 中国区域未来不同SSP情景下极端气候指数距平（相对于1961—1990年）随时间变化趋势（a） TX90p，（b） TN10p，（c） RX1day，（d） RX5day （极端温度指数为绝对变化，极端降水指数为相对变化）

Fig.4 Temporal trends of anomalies of extreme climate indices in China under different SSP scenarios （relative to 1961-1990）（a） TX90p，（b） TN10p，（c） RX1day，（d） RX5day （The extreme temperature indices are an absolute change， while the extreme precipitation indices are a relative change）

表3 2024—2100年AM与机器学习预估的中国区域极端气候指数不同排放情景下的未来变化趋势（相对于1961—1990年）

Tab.3 Future trends （2024-2100） of extreme climate indices over China estimated by the AM and machine learning under different emission scenarios （relative to 1961-1990）

指数	排放情景	AM	机器学习
TX90p	SSP1-2.6	+1.07	+0.95（Ridge）
	SSP2-4.5	+2.86	+2.62（Ridge）
	SSP5-8.5	+6.00	+5.49（Ridge）
TN10p	SSP1-2.6	-0.20	-0.19（RF）
	SSP2-4.5	-0.42	-0.41（RF）
	SSP5-8.5	-0.56	-0.54（RF）
RX1day	SSP1-2.6	+0.92	+1.01（ET）
	SSP2-4.5	+1.75	+1.63（ET）
	SSP5-8.5	+3.90	+3.76（ET）
RX5day	SSP1-2.6	+0.98	+1.22（ET）
	SSP2-4.5	+1.71	+1.81（ET）
	SSP5-8.5	+3.73	+3.92（ET）

图5 21世纪末期（2080—2099年）中国区域SSP1-2.6（左）、SSP2-4.5（中）、SSP5-8.5（右）情景下TX90p（上，基于Ridge模型）与TN10p（下，基于RF模型）的距平空间分布（相对于1961—1990年基准期）（单位：%）

Fig.5 Spatial distribution of anomalies of the TX90p （the top， based on the Ridge model） and the TN10p （the bottom， based on the RF model） over China under SSP1-2.6 （the left）， SSP2-4.5（the middle）， and SSP5-8.5 （the right） scenarios for the late 21st century （2080-2099）， relative to the 1961-1990 baseline （Unit： %）

图6 21世纪末期（2080—2099 年）中国区域SSP1-2.6（左）、SSP2-4.5（中）、SSP5-8.5（右）情景下，RX1day（上）与RX5day（下）距平空间分布（相对于1961—1990年）（单位：%）（均基于ET模型）

Fig.6 The spatial distribution of anomalies of the RX1day （the top） and the RX5day （the bottom） over China under SSP1-2.6 （the left）， SSP2-4.5 （the middle）， and SSP5-8.5 （the right） scenarios for the late 21st century （2080-2099）， relative to the 1961-1990 baseline （Unit： %）（based on the ET model）

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